torchvision

The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.

Installation

Please refer to the official instructions to install the stable versions of torch and torchvision on your system.

To build source, refer to our contributing page.

The following is the corresponding torchvision versions and supported Python versions.

`torch`	`torchvision`	Python
`main` / `nightly`	`main` / `nightly`	`>=3.8`, `<=3.12`
`2.3`	`0.18`	`>=3.8`, `<=3.12`
`2.2`	`0.17`	`>=3.8`, `<=3.11`
`2.1`	`0.16`	`>=3.8`, `<=3.11`
`2.0`	`0.15`	`>=3.8`, `<=3.11`

older versions

`torch`	`torchvision`	Python
`1.13`	`0.14`	`>=3.7.2`, `<=3.10`
`1.12`	`0.13`	`>=3.7`, `<=3.10`
`1.11`	`0.12`	`>=3.7`, `<=3.10`
`1.10`	`0.11`	`>=3.6`, `<=3.9`
`1.9`	`0.10`	`>=3.6`, `<=3.9`
`1.8`	`0.9`	`>=3.6`, `<=3.9`
`1.7`	`0.8`	`>=3.6`, `<=3.9`
`1.6`	`0.7`	`>=3.6`, `<=3.8`
`1.5`	`0.6`	`>=3.5`, `<=3.8`
`1.4`	`0.5`	`==2.7`, `>=3.5`, `<=3.8`
`1.3`	`0.4.2` / `0.4.3`	`==2.7`, `>=3.5`, `<=3.7`
`1.2`	`0.4.1`	`==2.7`, `>=3.5`, `<=3.7`
`1.1`	`0.3`	`==2.7`, `>=3.5`, `<=3.7`
`<=1.0`	`0.2`	`==2.7`, `>=3.5`, `<=3.7`

Image Backends

Torchvision currently supports the following image backends:

torch tensors
PIL images:
- Pillow
- Pillow-SIMD - a much faster drop-in replacement for Pillow with SIMD.

[UNSTABLE] Video Backend

Torchvision currently supports the following video backends:

pyav (default) - Pythonic binding for ffmpeg libraries.
video_reader - This needs ffmpeg to be installed and torchvision to be built from source. There shouldn’t be any conflicting version of ffmpeg installed. Currently, this is only supported on Linux.

conda install -c conda-forge 'ffmpeg<4.3'
python setup.py install

Using the models on C++

Refer to example/cpp.

DISCLAIMER: the libtorchvision library includes the torchvision custom ops as well as most of the C++ torchvision APIs. Those APIs do not come with any backward-compatibility guarantees and may change from one version to the next. Only the Python APIs are stable and with backward-compatibility guarantees. So, if you need stability within a C++ environment, your best bet is to export the Python APIs via torchscript.

Documentation

You can find the API documentation on the pytorch website: https://pytorch.org/vision/stable/index.html

Contributing

See the CONTRIBUTING file for how to help out.

Disclaimer on Datasets

This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset’s license.

If you’re a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!

Pre-trained Model License

The pre-trained models provided in this library may have their own licenses or terms and conditions derived from the dataset used for training. It is your responsibility to determine whether you have permission to use the models for your use case.

More specifically, SWAG models are released under the CC-BY-NC 4.0 license. See SWAG LICENSE for additional details.

Citing TorchVision

If you find TorchVision useful in your work, please consider citing the following BibTeX entry:

@software{torchvision2016,
    title        = {TorchVision: PyTorch's Computer Vision library},
    author       = {TorchVision maintainers and contributors},
    year         = 2016,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/pytorch/vision}}
}